Association of regular glucosamine use with incident dementia: evidence from a longitudinal cohort and Mendelian randomization study.

BACKGROUND: Emerging data suggests the neuroprotective and anti-neuroinflammatory effects of glucosamine. We aimed to examine the association between regular glucosamine use and risk of incident dementia, including dementia subtypes. METHODS: We conducted large-scale observational and two-sample Mendelian randomization (MR) analyses. Participants in UK Biobank having accessible data for dementia incidence and who did not have dementia at baseline were included in the prospective cohort. Through the Cox proportional hazard model, we examined the risks of incident all-cause dementia, Alzheimer's disease (AD), and vascular dementia among glucosamine users and non-users. To further test the causal association between glucosamine use and dementia, we conducted a 2-sample MR utilizing summary statistics from genome-wide association studies (GWAS). The GWAS data were obtained from observational cohort participants of mostly European ancestry. RESULTS: During a median follow-up of 8.9 years, there were 2458 cases of all-cause dementia, 924 cases of AD, and 491 cases of vascular dementia. In multivariable analysis, the hazard ratios (HR) of glucosamine users for all-cause dementia, AD, and vascular dementia were 0.84 (95% CI 0.75-0.93), 0.83 (95% CI 0.71-0.98), and 0.74 (95% CI 0.58-0.95), respectively. The inverse associations between glucosamine use and AD appeared to be stronger among participants aged below 60 years than those aged above 60 years (p = 0.04 for interaction). The APOE genotype did not modify this association (p > 0.05 for interaction). Single-variable MR suggested a causal relationship between glucosamine use and lower dementia risk. Multivariable MR showed that taking glucosamine continued to protect against dementia after controlling for vitamin, chondroitin supplement use and osteoarthritis (all-cause dementia HR 0.88, 95% CI 0.81-0.95; AD HR 0.78, 95% CI 0.72-0.85; vascular dementia HR 0.73, 95% CI 0.57-0.94). Single and multivariable inverse variance weighted (MV-IVW) and MR-Egger sensitivity analyses produced similar results for these estimations. CONCLUSIONS: The findings of this large-scale cohort and MR analysis provide evidence for potential causal associations between the glucosamine use and lower risk for dementia. These findings require further validation through randomized controlled trials.

SARS-CoV-2 Spike Protein Post-Translational Modification Landscape and Its Impact on Protein Structure and Function via Computational Prediction.

To elucidate the role of post-translational modifications (PTMs) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein's structure and virulence, we generated a high-resolution map of 87 PTMs using liquid chromatography with tandem mass spectrometry data on the extracted spike protein from SARS-CoV-2 virions and then reconstituted its structure heterogeneity caused by PTMs. Nonetheless, Alphafold2, a high-accuracy artificial intelligence tool to perform protein structure prediction, relies solely on primary amino acid sequence, whereas the impact of PTM, which often modulates critical protein structure and function, is much ignored. To overcome this challenge, we proposed the mutagenesis approach-an in silico, site-directed amino acid substitution to mimic the influence of PTMs on protein structure due to altered physicochemical properties in the post-translationally modified amino acids-and then reconstituted the spike protein's structure from the substituted sequences by Alphafold2. For the first time, the proposed method revealed predicted protein structures resulting from PTMs, a problem that Alphafold2 has yet to address. As an example, we performed computational analyses of the interaction of the post-translationally modified spike protein with its host factors such as angiotensin-converting enzyme 2 to illuminate binding affinity. Mechanistically, this study suggested the structural analysis of post-translationally modified protein via mutagenesis and deep learning. To summarize, the reconstructed spike protein structures showed that specific PTMs can be used to modulate host factor binding, guide antibody design, and pave the way for new therapeutic targets. The code and Supplementary Materials are freely available at https://github.com/LTZHKUSTGZ/SARS-CoV-2-spike-protein-PTM.

A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.

Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.

Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens.

(1) Background: The high use of antibiotics has made the issue of antimicrobial resistance (AMR) increasingly serious, which poses a substantial threat to the health of animals and humans. However, there remains a certain gap in the AMR system and risk assessment models between China and the advanced world level. Therefore, this paper aims to provide advanced means for the monitoring of antibiotic use and AMR data, and take piglets as an example to evaluate the risk and highlight the seriousness of AMR in China. (2) Methods: Based on the principal component analysis method, a drug resistance index model of anti-E. coli drugs was established to evaluate the antibiotic risk status in China. Additionally, based on the second-order Monte Carlo methods, a disease risk assessment model for piglets was established to predict the probability of E. coli disease within 30 days of taking florfenicol. Finally, a browser/server architecture-based visualization database system for animal-derived pathogens was developed. (3) Results: The risk of E. coli in the main area was assessed and Hohhot was the highest risk area in China. Compared with the true disease risk probability of 4.1%, the result of the disease risk assessment model is 7.174%, and the absolute error was 3.074%. Conclusions: Taking E. coli as an example, this paper provides an innovative method for rapid and accurate risk assessment of drug resistance. Additionally, the established system and assessment models have potential value for the monitoring and evaluating AMR, highlight the seriousness of antimicrobial resistance, advocate the prudent use of antibiotics, and ensure the safety of animal-derived foods and human health.